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ISM 50 - Business Information Systems
Lecture 16
Instructor: Magdalini EirinakiUC Santa CruzMay 24, 2007
Outline
Announcements MySQL case (cont’d) Student Presentation - FedEx Student Presentation - UPS OLTP OLAP
Announcements Reading for 5/29
Messerchmitt 11.2 (pp.333-335) Messerchmitt 18.1 (pp.498 - 505) Akamai Case (reader pp.217-236)
(Read book first)
Forthcoming Student Presentations 5/29
Evan Price (business paper) Matthew Payne (Akamai case)
5/31 Eric Gonzalez (business paper: WalMart) Derek Stern (business paper: Home Depot)
News Folio #3 due next Thursday (5/31)
mySQLWhat does mySQL make?
How Successful is mySQL? Visibility: Fortune magazine, more mentions on www Reaction from giants Revenue growth 2001 700k, 2002 6.2m, 2003 10m Good performance reviews Recent SAP alliance But Market share tiny:
$10 million out of $10 billion market!
Why Success? Good Technology Large DBMS bloated with features most don’t need Innovative OSS model
mySQLHow does OSS work?
Two Types of License: GPL
Free No Support Any software that uses MySQL as a module must itself be
made GPL Commercial License
Support Could be distributed with non-open source software Not Free:
MySQL: Classic $250, Pro $495 (for ~ 50 users) Compare to:
MSFT $3150 single proc for 50 users IBM $33000 single proc for 50 users Oracle $40000 single proc for 50 users
Aside: DB’s in different software stacks
Operating System
Middleware(DBMS)
Application
MS Windows or other OS
Oracleor MySQL, IBM, etc
SAPOr Oracle, Axtapa, etc.
ERPSoftware
Stack
Linux or other OS
MySQL or other DB
Apache Web Server
WebApplication
SoftwareStack
Proprietary Business Logic
BankingSoftware
Stack
IBM z/OSor other OS
GeneralSoftware
Stack
Oracleor other DB
ProprietaryBanking App.
Which companies are competitors? Which are complimentary to each other? Which are both?
mySQL
Which segments of market is mySQLstrong in? Large Companies or Small Companies? Web applications or Critical Enterprise data?
Why would a major enterprise want to payso much more for an Oracle or IBM DB?
My SQL: market
Small 20% Medium 30% Large 50%
Enterprise wide data 90%
Web Sites 10%
Microsoft Oracle IBM
My SQL
ReliabilityScalabilitySupportLongevity
Cost
Figure Adapted from “Teaching Note for MySQL Open Source Database,” 6/1/04, Stanford GSB.
How should mySQL grow in order to meet it’s stated goal of getting to $100 million In revenue?
My SQL: Growth Strategy
- Lack of Brand identity in this segement - MySQL lacks the organization to offer support - Large enterprises have high switching costs
Small 20% Medium 30% Large 50%
Enterprise wide data 90%
Web Sites 10%
Microsoft Oracle IBM
My SQL
ReliabilityScalabilitySupportLongevity
Cost
Figure Adapted from “Teaching Note for MySQL Open Source Database,” 6/1/04, Stanford GSB.
My SQL: Growth Strategy
- Not a big enough market to reach stated $100 million goal.
Small 20% Medium 30% Large 50%
Enterprise wide data 90%
Web Sites 10%
Microsoft Oracle IBM
My SQL
ReliabilityScalabilitySupportLongevity
Cost
Figure Adapted from “Teaching Note for MySQL Open Source Database,” 6/1/04, Stanford GSB.
Stay Put?
My SQL: Growth Strategy
+ Many of these customers already using MySQL with websites + Less emphasis on global organization + Leverage SAP alliance - Up against Microsoft.
Small 20% Medium 30% Large 50%
Enterprise wide data 90%
Web Sites 10%
Microsoft Oracle IBM
My SQL
ReliabilityScalabilitySupportLongevity
Cost
Figure Adapted from “Teaching Note for MySQL Open Source Database,” 6/1/04, Stanford GSB.
Maybe?
My SQL: Growth Strategy
+ builds on existing brand and strengths - Market not so big
Small 20% Medium 30% Large 50%
Enterprise wide data 90%
Web Sites 10%
Microsoft Oracle IBM
My SQL
ReliabilityScalabilitySupportLongevity
Cost
Figure Adapted from “Teaching Note for MySQL Open Source Database,” 6/1/04, Stanford GSB.
Maybe?
Databases
Application I Application II
Aggregation: accessingmultiple databases
Sharing: two or moreapplications accessing thesame databases
Recall - Two capabilities
Travelocity.com CheapTickets.com
Example - Travel Agency
Hotels Cars Airtickets
A resource might be unavailable
Travelocity.com CheapTickets.com
Example - Travel Agency
Hotels Cars Airtickets
Two applications might try to access & update the same resourceconcurrently
Travelocity.com CheapTickets.com
Example - Travel Agency
Hotels Cars Airtickets
An application or a host might crash before the completion of thetransaction
Travelocity.com CheapTickets.com
Example - Travel Agency
Hotels Cars Airtickets
A customer’s transaction should be completed in its entirety,or aborted
Transaction Processing
“The coordination of multiple resources and theshared access to common resources in a systematicand consistent way”
Examples? Financial applications (stock market, ATMs) Reservations (travel, theatre) Manufacturing (inventory, purchasing, billing) Etc…
Online Transaction Processing(OLTP) Transaction Processing for networked applications
4 Important Properties of transactions: ACID Atomicity Consistency Isolation Durability
The ACID properties
Atomicity All transaction components should either complete together
(commit) or abort E.g. All reservations (airline, hotel, car) should be grouped as a single
transaction that either commits, or aborts Consistency
A transaction must leave the system in a consistent state at the endof the transaction, or else abort
E.g. Either a consistent set of reservations has been made, or none Isolation
Concurrent transactions are allowed only if they don’t interfere witheach other
Two travel agents can concurrently access the same database if thereservations are for different dates/places
Durability A transaction leaves the resources in a permanent state after it
commits
Structure of a Transaction
DurableStarting State
Actions to beperformed
Durable,ConsistentEnd State
Successful completion
Abort
Rollback
OLTP
Simplifies application development
Ensables protection and integrity of mission-criticaldata in a transparent way for the end user for the application developer
Data warehouses &OLAP
“Data Mining: Concepts andTechniques”Jiawei Han©2006 Jiawei Han and Micheline Kamber,All rights reserved
Recall - Decision Support
Knowledge management systems: Turn dataand information into knowledge A Data warehouse stores organization’s historical
data
Provide functionalities for summarizing, aggregating,reporting on these data
Data mining is the process of discovering patternsin large amounts of data
What is a Data Warehouse?
A decision support database that is maintainedseparately from the organization’s operationaldatabase
Focusing on the modeling and analysis of data fordecision makers, not on daily operations or transactionprocessing
Slide adapted from slides for Data Mining: Concepts and TechniquesBy Jiawei Han and Micheline Kamber. Copyright 2006. See copyright notice
Data Warehouse—Nonvolatile
Operational update of data does not occur in thedata warehouse environment Does not require transaction processing, recovery, and
concurrency control mechanisms
Requires only two operations in data accessing:
initial loading of data and access of data
Slide adapted from slides for Data Mining: Concepts and TechniquesBy Jiawei Han and Micheline Kamber. Copyright 2006. See copyright notice
Data Warehouse—Time Variant
The time horizon for the data warehouse is significantlylonger than that of operational systems Operational database: current value data Data warehouse data: provide information from a historical
perspective (e.g., past 5-10 years)
Every key structure in the data warehouse Contains an element of time, explicitly or implicitly
Slide adapted from slides for Data Mining: Concepts and TechniquesBy Jiawei Han and Micheline Kamber. Copyright 2006. See copyright notice
Data Warehouse vs. OperationalDBMSOLTP (on-line transaction processing)
Major task of traditional relational DBMS Day-to-day operations: purchasing, inventory, banking, manufacturing,
payroll, registration, accounting, etc.OLAP (on-line analytical processing)
Major task of data warehouse system Data analysis and decision making
Distinct features (OLTP vs. OLAP): User and system orientation: customer vs. market Data contents: current, detailed vs. historical, consolidated Database design: Relational (ER) vs. Star View: current, local vs. evolutionary, integrated Access patterns: update vs. read-only but complex queries
Slide adapted from slides for Data Mining: Concepts and TechniquesBy Jiawei Han and Micheline Kamber. Copyright 2006. See copyright notice
OLTP vs. OLAP
OLTP OLAP
users clerk, IT professional knowledge worker
function day to day operations decision support
DB design application-oriented subject-oriented
data current, up-to-date
detailed, flat relational
isolated
historical,
summarized, multidimensional
integrated, consolidated
usage repetitive ad-hoc
access read/write
index/hash on prim. key
lots of scans
unit of work short, simple transaction complex query
# records accessed tens millions
#users thousands hundreds
DB size 100MB-GB 100GB-TB
metric transaction throughput query throughput, response
Slide adapted from slides for Data Mining: Concepts and TechniquesBy Jiawei Han and Micheline Kamber. Copyright 2006. See copyright notice
Why Separate Data Warehouse?High performance for both systems
DBMS— tuned for OLTP: access methods, indexing,concurrency control, recovery
Warehouse—tuned for OLAP: complex OLAP queries,multidimensional view, consolidation
Note: There are more and more systems which performOLAP analysis directly on relational databases
Slide adapted from slides for Data Mining: Concepts and TechniquesBy Jiawei Han and Micheline Kamber. Copyright 2006. See copyright notice